1. Field of the Present Methods and Devices
The present methods and devices relate generally to the fields of segmentation and smoothing. More particular, they relate to segmenting a volume of interest (e.g., lung parenchyma) from a volumetric dataset of images, and then performing smoothing operations on certain portions of the volume of interest (e.g., the mediastinal boundary of the lung parenchyma) based on certain criteria (e.g., the airway tree).
2. Description of Related Art
Automatic lung segmentation in volumetric computed tomography (CT) images has been extensively investigated, and several methods have been proposed. Most methods distinguish the lung parenchyma from the surrounding anatomy based on the difference in CT attenuation values. This leads to an irregular and inconsistent lung boundary for the regions near the mediastinum, which can cause inconsistent boundaries both across subjects, and within subjects scanned at different intervals of time. Processes like lung image registration and lung atlas construction can be affected by such inconsistencies.
The first step in any CT-image based pulmonary analysis is the identification of the lungs. Given the large size of CT datasets, manually segmenting the lungs is tedious and prone to interobserver variations. There are a number of computer-assisted and fully automatic methods that have been proposed for human lung segmentation from CT images. Brown et al. [1] proposed a knowledge-based approach to the segmentation problem, with an anatomical model, an inference engine, and image processing routines which communicate with them. Hu et al. [2] proposed a fully automatic method for lung extraction, left and right lung separation, and lung contour smoothing.
Most of the techniques for segmenting the lungs in CT images use gray-level processing to distinguish between the low density lung regions and denser surrounding tissue. The lung regions near the mediastinum contain the radiodense pulmonary arteries and veins, which are excluded from the lung regions due to this processing (see FIGS. 1A and 1B). This gives rise to indentations in the surface of the lungs near the mediastinum. When manually tracing the lung contours, a manual analyst may trace across the large pulmonary vessels and group them with the lung regions (FIG. 1C), yielding a smooth lung contour. Manual editing of this sort can be extremely time consuming given that a typical dataset may contain 500 slices or more. Also, because manual editing is typically done on one cross-sectional view at a time, i.e., transverse, coronal or sagittal, it does not ensure 3-D smoothness of the lung boundary.
A similar situation occurs as the mainstem bronchi merge into the lungs. Gray-scale thresholding may include these large airways with the lung regions because of their low attenuation values partial volume effects (see FIGS. 1D and 1E). This behavior may not be consistent across slices, so a big airway segment included in one slice may be missing on another slice. This causes the lung boundary to have an irregular appearance, especially when seen in the coronal and sagittal views (FIG. 1F). Manual analysts may trace lung borders around the airways, or they trace across them. These factors can be a source of problems in applications such as vascular tree segmentation and 3-D lung registration, which depend on consistent lung boundaries.
Hu et al. [2] suggested a lung contour smoothing method as an optional, post-segmentation step. They identified three major reasons for unsmoothness near the mediastinum: the pulmonary vessels; the large airways; and the small airways. For each of these cases a series of binary morphology and connected component analysis based steps were given. All these operations were performed in 2-D on transverse slices only, therefore unsmoothness remained in other views.
As part of their work on pulmonary nodule detection, Armato et al. [3] use a rolling ball algorithm followed by linear interpolation to fill in indentations in the lung contour. These operations are also performed on transverse slices.
Li et al. [4] proposed a method for automatic lung identification in chest radiographs which incorporates a mediastinal smoothing step involving a N point averaging operator applied to edge pixels. This work has not been extended to 3-D CT images.